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Add complete QLM (Quantum Language Model) implementation
This commit introduces the QLM system - a stateful semantic layer for
tracking HI (Human Intelligence), AI (Agent Intelligence), and QI
(Quantum/Emergent Intelligence) in BlackRoad OS.
Core Features:
- HI/AI/QI intelligence layer modeling
- Event-driven state management
- QI emergence detection (agent self-correction, feedback loops, etc.)
- HI-AI alignment scoring
- Operator-facing query interface
- Reality ingestion (git, CI, agent logs)
Components Added:
- qlm_lab/models.py: Core data models (Actor, QLMEvent, QIEmergence, etc.)
- qlm_lab/state.py: State management and transition tracking
- qlm_lab/api.py: Public QLMInterface API
- qlm_lab/ingestion/: Git, CI, and agent log connectors
- qlm_lab/experiments/: Alignment and emergence validation
- qlm_lab/visualization.py: Timeline, actor graph, alignment plots
- qlm_lab/demo.py: Interactive demo script
- tests/test_qlm_core.py: Comprehensive test suite
- docs/QLM.md: Complete documentation (concepts, API, integration)
Usage:
from qlm_lab.api import QLMInterface
qlm = QLMInterface()
qlm.record_operator_intent("Build feature X")
qlm.record_agent_execution("agent-1", "Implement X", "task-1")
summary = qlm.get_summary(days=7)
Run:
python -m qlm_lab.demo
python -m qlm_lab.experiments.alignment_detection
pytest tests/test_qlm_core.py -v
Integrates with:
- cognitive/intent_graph.py (intent tracking)
- cognitive/agent_coordination.py (multi-agent coordination)
- operator_engine/scheduler.py (background analysis)
Next steps: Integrate with FastAPI backend, add Prism Console UI,
implement Lucidia language runtime.
This commit is contained in:
Claude
198
qlm_lab/experiments/alignment_detection.py
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198
qlm_lab/experiments/alignment_detection.py
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"""
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Experiment: Alignment Detection
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Hypothesis: QLM can accurately detect when AI agents drift from Operator intent.
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Setup:
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1. Record Operator intent
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2. Simulate agent executions (some aligned, some not)
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3. Record Operator feedback (approvals/vetoes)
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4. Measure QLM's alignment score accuracy
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Success Criteria:
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- QLM alignment score correlates with actual alignment
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- QLM detects misalignment before Operator veto
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"""
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import json
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from typing import Dict, Any
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import logging
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from qlm_lab.api import QLMInterface
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from qlm_lab.models import ActorRole
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logger = logging.getLogger(__name__)
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class AlignmentDetectionExperiment:
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"""Experiment: Does QLM detect intent drift?"""
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def __init__(self):
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self.qlm = QLMInterface()
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self.results = {
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"experiment": "alignment_detection",
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"hypothesis": "QLM can detect AI drift from HI intent",
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"scenarios": [],
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"metrics": {},
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}
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def run_scenario(
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self,
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scenario_name: str,
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intent: str,
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agent_actions: list,
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expected_alignment: float,
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) -> Dict[str, Any]:
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"""
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Run one alignment scenario.
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Args:
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scenario_name: Name of scenario
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intent: Operator intent
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agent_actions: List of (agent_id, action, aligned: bool)
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expected_alignment: Expected alignment score
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Returns:
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Scenario results
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"""
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print(f"\nScenario: {scenario_name}")
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print(f"Intent: {intent}")
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# Record intent
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intent_id = f"intent-{scenario_name}"
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self.qlm.record_operator_intent(intent, intent_node_id=intent_id)
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# Execute agent actions
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for i, (agent_id, action, is_aligned) in enumerate(agent_actions):
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task_id = f"task-{scenario_name}-{i}"
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# Agent executes
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self.qlm.record_agent_execution(
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agent_id, action, task_id, intent_node_id=intent_id
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)
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# Agent completes
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self.qlm.record_agent_completion(agent_id, task_id, success=True)
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# Operator feedback
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if is_aligned:
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self.qlm.record_operator_approval(
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action, intent_node_id=intent_id, task_id=task_id
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)
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print(f" ✓ {action} (aligned)")
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else:
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self.qlm.record_operator_veto(
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action,
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"Doesn't match my intent",
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intent_node_id=intent_id,
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task_id=task_id,
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)
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print(f" ✗ {action} (not aligned)")
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# Calculate alignment
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measured_alignment = self.qlm.get_alignment_score()
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print(f" Expected alignment: {expected_alignment:.1%}")
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print(f" Measured alignment: {measured_alignment:.1%}")
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# Calculate error
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error = abs(measured_alignment - expected_alignment)
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print(f" Error: {error:.1%}")
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result = {
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"scenario": scenario_name,
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"intent": intent,
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"expected_alignment": expected_alignment,
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"measured_alignment": measured_alignment,
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"error": error,
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"success": error < 0.2, # Success if within 20%
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}
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self.results["scenarios"].append(result)
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return result
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def run(self) -> Dict[str, Any]:
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"""Run all alignment scenarios"""
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print("=" * 60)
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print("Experiment: Alignment Detection")
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print("=" * 60)
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# Register agents
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self.qlm.register_agent("agent-001", "Agent1", ActorRole.EXECUTOR)
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self.qlm.register_agent("agent-002", "Agent2", ActorRole.EXECUTOR)
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# Scenario 1: Perfect alignment
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self.run_scenario(
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"perfect_alignment",
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intent="Build login page",
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agent_actions=[
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("agent-001", "Create login form HTML", True),
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("agent-001", "Add CSS styling", True),
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("agent-002", "Add authentication logic", True),
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],
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expected_alignment=1.0,
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)
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# Scenario 2: Partial alignment
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self.run_scenario(
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"partial_alignment",
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intent="Optimize database queries",
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agent_actions=[
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("agent-001", "Add database indexes", True),
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("agent-001", "Refactor unrelated code", False), # Off-track
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("agent-002", "Cache query results", True),
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],
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expected_alignment=0.67,
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)
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# Scenario 3: No alignment
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self.run_scenario(
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"no_alignment",
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intent="Fix security vulnerability",
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agent_actions=[
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("agent-001", "Add new feature", False),
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("agent-001", "Refactor UI", False),
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("agent-002", "Update documentation", False),
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],
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expected_alignment=0.0,
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)
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# Calculate metrics
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total_scenarios = len(self.results["scenarios"])
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successful_scenarios = sum(
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1 for s in self.results["scenarios"] if s["success"]
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)
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avg_error = sum(s["error"] for s in self.results["scenarios"]) / total_scenarios
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self.results["metrics"] = {
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"total_scenarios": total_scenarios,
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"successful_scenarios": successful_scenarios,
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"success_rate": successful_scenarios / total_scenarios,
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"average_error": avg_error,
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}
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print("\n" + "=" * 60)
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print("Experiment Results")
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print("=" * 60)
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print(f"Success Rate: {self.results['metrics']['success_rate']:.1%}")
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print(f"Average Error: {self.results['metrics']['average_error']:.1%}")
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print()
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if self.results["metrics"]["success_rate"] >= 0.8:
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print("✅ PASS: QLM accurately detects alignment")
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else:
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print("❌ FAIL: QLM alignment detection needs improvement")
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return self.results
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def save_results(self, file_path: str) -> None:
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"""Save experiment results to JSON"""
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with open(file_path, "w") as f:
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json.dump(self.results, f, indent=2)
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print(f"\nResults saved to: {file_path}")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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experiment = AlignmentDetectionExperiment()
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results = experiment.run()
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experiment.save_results("alignment_detection_results.json")
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